Representational information: a new general notion and measure\ud of information

In what follows, we introduce the notion of representational information (information conveyed by sets of dimensionally defined objects about their superset of origin) as well as an\ud original deterministic mathematical framework for its analysis and measurement. The framework, based in part on categorical invariance theory [Vigo, 2009], unifies three key constructsof universal science – invariance, complexity, and information. From this unification we define the amount of information that a well-defined set of objects R carries about its finite superset of origin S, as the rate of change in the structural complexity of S (as determined by its degree of categorical invariance), whenever the objects in R are removed from the set S. The measure captures deterministically the significant role that context and category structure play in determining the relative quantity and quality of subjective information conveyed by particular objects in multi-object stimuli

A knowledge engineering approach to the recognition of genomic coding regions

ได้ทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ปีงบประมาณ พ.ศ.2556-255

Toward boosting distributed association rule mining by data de-clustering

[[abstract]]Existing parallel algorithms for association rule mining have a large inter-site communication cost or require a large amount of space to maintain the local support counts of a large number of candidate sets. This study proposes a de-clustering approach for distributed architectures, which eliminates the inter-site communication cost, for most of the influential association rule mining algorithms. To de-cluster the database into similar partitions, an efficient algorithm is developed to approximate the shortest spanning path (SSP) to link transaction data together. The SSP obtained is then used to evenly de-cluster the transaction data into subgroups. The proposed approach guarantees that all subgroups are similar to each other and to the original group. Experiment results show that data size and the number of items are the only two factors that determine the performance of de-clustering. Additionally, based on the approach, most of the influential association rule mining algorithms can be implemented in a distributed architecture to obtain a drastic increase in speed without losing any frequent itemsets. Furthermore, the data distribution in each de-clustered participant is almost the same as that of a single site, which implies that the proposed approach can be regarded as a sampling method for distributed association rule mining. Finally, the experiment results prove that the original inadequate mining results can be improved to an almost perfect level